<![CDATA[This study aims to conduct a comprehensive statistical evaluation of photovoltaic inverter performance under various solar irradiance conditions to identify operational characteristics and develop predictive performance models. The research methodology employs a quantitative approach with experimental design through primary data collection over 12 months using an integrated data acquisition system with pyranometer irradiance sensors, temperature sensors, and power analyzers on a 10 kW string inverter system. Data were categorized into five irradiance groups and analyzed using descriptive statistics, correlation analysis, polynomial regression, and ANOVA with SPSS and MATLAB software. Results show the highest average efficiency in the high irradiance category (96.9%) with the lowest coefficient of variation in moderate irradiance (2.19%). Strong positive correlation between irradiance and inverter efficiency (r = 0.762) and cubic regression predictive model demonstrate high accuracy (R² = 0.912, RMSE 1.89%). Variance analysis proves significant differences between irradiance categories (F = 1,234.56, p < 0.001) with 78.5% effect size. Temporal patterns show systematic seasonal variation with peak efficiency in May (96.2%) and annual-daily periodicity correlating with natural irradiance cycles. The research confirms the importance of statistical characterization for photovoltaic inverter performance optimization and development of adaptive operational strategies based on irradiance conditions. Keywords: photovoltaic inverter, statistical analysis, solar irradiance, energy efficiency, predictive model]]>
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